Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Mining Mutually Dependent Ordered Subtrees in Tree Databases
New Frontiers in Applied Data Mining
Mining induced and embedded subtrees in ordered, unordered, and partially-ordered trees
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
POTMiner: mining ordered, unordered, and partially-ordered trees
Knowledge and Information Systems
Frequent tree pattern mining: A survey
Intelligent Data Analysis
Model guided algorithm for mining unordered embedded subtrees
Web Intelligence and Agent Systems
Mining patterns from longitudinal studies
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Mining Induced/Embedded Subtrees using the Level of Embedding Constraint
Fundamenta Informaticae
| Hi-index | 0.00 |
Frequent subtree mining has become increasingly important in recent years. In this paper, we present AMIOT algorithm to discover all frequent ordered subtrees in a tree-structured database. In order to avoid the generation of infrequent candidate trees, we propose the techniques such as right-and-left tree join and serial tree extension. Proposed methods enumerate only the candidate trees with high probability of being frequent without any duplications. The experiments on synthetic dataset and XML database show that AMIOT reduces redundant candidate trees and outperforms FREQT algorithm by up to five times in execution time.